Modeling And Optimization Of CO₂ Capture,Transportation,Oil Displacement And Storage Process

With the rapid development of the global economy,people's consumption of fossil energy is also growing.The threat of global climate change caused by greenhouse gases such as CO₂ is increasing.Carbon capture,storage and utilization(CCUS)technology has attracted widespread attention from academia and industry as a key technology for effectively mitigating CO₂ emissions.The economy of CCUS technology has always been a major factor affecting its large-scale development in China and even in the world.Therefore,it is of great theoretical and practical significance to use scientific methods to optimize the design of CCUS whole process,construct a reasonable technical implementation plan,improve the economy of CCUS technology,and provide scientific decision-making basis for China's emission reduction strategy.The whole process of CCUS includes three subsystems:CO₂ capture,CO₂ transport,oil displacement and storage.Based on the establishment of three subsystem engineering-economic models,the CCUS full process optimization model is constructed.Taking CCUS total cost as the objective function,CO₂ capture amount,pipeline inlet pressure and well depth as decision variables,the whole process optimization problem of CCUS based on genetic algorithm is proposed.The simulation results show the effectiveness of the algorithm and the implementation of CCUS.Provide a certain theoretical basis.In a full-process optimization system considering multiple variable design conditions,the changing design conditions make the general optimization algorithm unable to solve the optimization problem.In order to solve the problem that the system of variable design conditions is difficult to solve,this paper proposes a robust optimization modeling method to transform the uncertainty of the system into the constraint condition of the optimization problem.Since the mathematical model under the condition of variable design is a complex nonlinear optimization problem,the key operators in the traditional genetic algorithm are improved to overcome the problem of low convergence when the traditional genetic algorithm solves the CCUS optimization problem.A new improved multi-objective genetic algorithm is proposed to solve the problem of no solution and leakage in the whole process of system optimization under multiple technical indicators.By improving the key operator crossover operator in the multi-objective genetic algorithm,the operator value can be automatically adjusted with the evolution process,which can achieve the convergence speed of the algorithm while maintaining the diversity of the population.The superior performance of the proposed algorithm is verified by comparison with actual cases.Through the research,the parameter configuration scheme of CCUS whole process system without considering a variety of variable design conditions,considering a variety of variable design conditions and multiple technical index requirements is obtained.By comparing the schemes obtained by several optimization methods with those without optimization,the excellent performance of the proposed optimization method is verified,which can provide some theoretical reference for the implementation of practical CCUS technology.